32 citations · 88 across the 23 of their papers we have counts for
11 papers · 1 filter
Flatness and Gradient Alignment Are Both Necessary: Spectral-Aware Gradient-Aligned Exploration for Multi-Distribution Learning
Aristotelis Ballas, Christos Diou
Sharpness-aware and gradient-alignment methods have been shown to improve generalization, however each family of methods targets a single geometric property of the loss landscape,…
Interpretability-by-Design with Accurate Locally Additive Models and Conditional Feature Effects
Vasilis Gkolemis, Loukas Kavouras, Dimitrios Kyriakopoulos +5
Generalized additive models (GAMs) offer interpretability through independent univariate feature effects but underfit when interactions are present in data. GAMs add selected p…
Fast and Robust Simulation-Based Inference With Optimization Monte Carlo
Vasilis Gkolemis, Christos Diou, Michael U. Gutmann
Bayesian parameter inference for complex stochastic simulators is challenging due to intractable likelihood functions. Existing simulation-based inference methods often require lar…
Gradient-Guided Annealing for Domain Generalization
Aristotelis Ballas, Christos Diou
Domain Generalization (DG) research has gained considerable traction as of late, since the ability to generalize to unseen data distributions is a requirement that eludes even stat…
Effector: A Python package for regional explanations
Vasilis Gkolemis, Christos Diou, Dimitris Kyriakopoulos +10
Effector is a Python package for interpreting machine learning (ML) models that are trained on tabular data through global and regional feature effects. Global effects, like Partia…
Which Augmentation Should I Use? An Empirical Investigation of Augmentations for Self-Supervised Phonocardiogram Representation Learning
Aristotelis Ballas, Vasileios Papapanagiotou, Christos Diou
Despite recent advancements in deep learning, its application in real-world medical settings, such as phonocardiogram (PCG) classification, remains limited. A significant barrier i…
Regionally Additive Models: Explainable-by-design models minimizing feature interactions
Vasilis Gkolemis, Anargiros Tzerefos, Theodore Dalamagas +2
Generalized Additive Models (GAMs) are widely used explainable-by-design models in various applications. GAMs assume that the output can be represented as a sum of univariate funct…
RHALE: Robust and Heterogeneity-aware Accumulated Local Effects
Vasilis Gkolemis, Theodore Dalamagas, Eirini Ntoutsi +1
Accumulated Local Effects (ALE) is a widely-used explainability method for isolating the average effect of a feature on the output, because it handles cases with correlated feature…
Detection of Anomalies in Multivariate Time Series Using Ensemble Techniques
Anastasios Iliopoulos, John Violos, Christos Diou +1
Anomaly Detection in multivariate time series is a major problem in many fields. Due to their nature, anomalies sparsely occur in real data, thus making the task of anomaly detecti…
DALE: Differential Accumulated Local Effects for efficient and accurate global explanations
Vasilis Gkolemis, Theodore Dalamagas, Christos Diou
Accumulated Local Effect (ALE) is a method for accurately estimating feature effects, overcoming fundamental failure modes of previously-existed methods, such as Partial Dependence…
A Domain Generalization Approach for Out-Of-Distribution 12-lead ECG Classification with Convolutional Neural Networks
Aristotelis Ballas, Christos Diou
Deep Learning systems have achieved great success in the past few years, even surpassing human intelligence in several cases. As of late, they have also established themselves in t…